Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.
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This work was supported, in part, by the Department of Veterans Affairs Center of Excellence for Suicide Prevention and the Precision Treatment of Mental Disorders Initiative. The contents are solely the responsibility of the authors and do not necessarily represent the views of the Veteran’s Health administration. The authors appreciate the helpful comments of Matthew K. Nock, Vicki Shahly, Murray B. Stein, and Robert J. Ursano on an earlier version of the paper.
Conflict of interest
In the past 3 years, RCK was a consultant for Johnson & Johnson Wellness and Prevention, Sage, Shire, and Takeda and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. Kessler is a co-owner of DataStat, Inc., a market research firm that carries out health care research. In the past 3 years, JRZ was a consultant for Johnson & Johnson. The remaining authors declare that they have no conflict of interest.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Kessler, R.C., Bossarte, R.M., Luedtke, A. et al. Suicide prediction models: a critical review of recent research with recommendations for the way forward. Mol Psychiatry 25, 168–179 (2020). https://doi.org/10.1038/s41380-019-0531-0
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